code: def _get_wg_embedding(
      feat_obj, wg_comm=None, wg_type=None, wg_location=None, **kwargs
  ):
      wg_comm_obj = wg_comm or wgth.get_local_node_communicator()
      wg_type_str = wg_type or "distributed"
      wg_location_str = wg_location or "cuda"
  
      if isinstance(feat_obj, (cudf.DataFrame, pd.DataFrame)):
          th_tensor = _cast_to_torch_tensor(feat_obj.values)
      else:
          th_tensor = _cast_to_torch_tensor(feat_obj)
      wg_embedding = wgth.create_embedding(
          wg_comm_obj,
          wg_type_str,
          wg_location_str,
          th_tensor.dtype,
          th_tensor.shape,
      )
      (
          local_wg_tensor,
          local_ld_offset,
      ) = wg_embedding.get_embedding_tensor().get_local_tensor()
      local_th_tensor = th_tensor[
          local_ld_offset : local_ld_offset + local_wg_tensor.shape[0]
      ]
      local_wg_tensor.copy_(local_th_tensor)
      wg_comm_obj.barrier()
      return wg_embedding
  
file_path: cugraph/python/cugraph/cugraph/gnn/feature_storage/feat_storage.py
imports: from __future__ import annotations
  from collections import defaultdict
  from typing import Sequence, Union
  import cudf
  import cupy
  import numpy
  import pandas
  from cugraph.utilities.utils import import_optional, MissingModule
label_desc: Use image node_id 1 for calling a global function with example usage: _get_wg_embedding(feat_obj, wg_comm, wg_type, wg_location) and returns: wg_embedding